In smart manufacturing, the automation of anomaly detection is essential for increasing productivity. Timeseries data from production processes are often complex sequences and their assessment involves many variables. Thus, anomaly detection with deep learning approaches is considered as an efficient and effective methodology. In this work, anomaly detection with deep autoencoders is examined. Three autoencoders are employed to analyze an industrial dataset and their performance is assessed. Autoencoders based on long short-term memory and convolutional neural networks appear to be the most promising.

Purpose: e-Business (e-B) organization is an integrated synergy of internet and supply chain. It is an organizational environment which consists of associations among suppliers, customers, work force and information technology. An important issue for an e-B organization is the comprehension of the critical factors that affect its success, in order to realize its full advantages and how it results in efficiency improvements of the organizations. The purpose of this paper is twofold: first to identify the critical success factors (CSFs) that determine the successful implementation of e-B and second to develop an integrated e-B model based on the CSFs.Design/methodology/approach: By following a quantitative approach, primary data were collected through a structured questionnaire from 174 Greek e-B firms. An initial exploratory factor analysis was conducted, followed by confirmatory factor analysis (CFA) and structural equation modeling (SEM).Findings: Findings have shown that there was a direct association of the Resource Based View (RBV) on the supply chain view (SCV) of a firm, which in turn influenced the performance/results of an e-B organization. It is concluded that e-B performance results and value creation are linked to customers’ and partners’ orientation (SCV) and human resources and information technology (RBV).Originality/value: The proposed framework contributes to the understanding of the successful implementation of e-B, by proposing a fifthfold framework. The proposed framework portrayed a very fundamental result, that the resourced based view and the SCV of the organization are the critical priorities and have a direct and positive affect on the performance and efficiency of the successful implementation of e-B.

Industry 4.0 has revolutionized the way companies manufacture, improve, and distribute their products through the use of new technologies, such as artificial intelligence, robotics, and machine learning. Autonomous Mobile Robots (AMRs), especially, have gained a lot of attention, supporting workers with daily industrial tasks and boosting overall performance by delivering vital information about the status of the production line. To this end, this work presents the novel Q-CONPASS system that aims to introduce AMRs in production lines with the ultimate goal of gathering important information that can assist in production and safety control. More specifically, the Q-CONPASS system is based on an AMR equipped with a plethora of machine learning algorithms that enable the vehicle to safely navigate in a dynamic industrial environment, avoiding humans, moving machines, and stationary objects while performing important tasks. These tasks include the identification of the following: (i) missing objects during product packaging and (ii) extreme skeletal poses of workers that can lead to musculoskeletal disorders. Finally, the Q-CONPASS system was validated in a real-life environment (i.e., the lift manufacturing industry), showcasing the importance of collecting and processing data in real-time to boost productivity and improve the well-being of workers.

Manufacturing as a Service (MaaS) refers to a set of tools and processes that can assist the shared use of networked production facilities. In the core of this paradigm is a vision where manufacturing environments shall profit from an online set of tools and services that can be tailored to the requirements coming from the different manufacturers, thus reaching a higher degree of flexibility and an increase in production efficiency. 320In the context of MaaS, the Horizon 2020 European Connected Factory Platform for Agile Manufacturing (EFPF) provides an operational instantiation of a large-scale MaaS across Europe, integrating a diverse set of services such as data analytics, factory connectors, and an interoperable Data Spine to proportionate a high level of automation across different shop-floors. This chapter explains the EFPF MaaS concept, going over its architectural design, and giving insight into how developers and SMEs can profit from the EFPF open-source SDK to generate new products, and how these products can be integrated into the EFPF broad marketplace. The chapter gives insight also to the different pilots developed in the project, explaining challenges faced, and proposed solutions. <!----> <!----> <!----> <!---->

Background: In the pursuit of sustainable manufacturing logistics, the integration of human–robot collaboration (HRC) into shop floors with legacy systems remains a significant challenge due to dynamic environments that complicate task and action planning. To address this challenge, this study investigates a sustainable HRC workstation implemented within a real-world electrical panel assembly use case in the lift manufacturing sector. Methods: The article examines the role of an AI-enabled dynamic task and action planning tool that coordinates human and robotic resources. The proposed solution integrates collaborative robotics using a multi-criteria scheduling algorithm alongside real-time replanning capabilities. Furthermore, the study proposes an evaluation framework based on the triple bottom line (TBL) approach to assess the developed solution. Results: The findings suggest that executing assembly tasks through this collaborative method supports sustainable operations scheduling for all resources. Conclusions: Ultimately, the proposed HRC solution demonstrates the potential to simultaneously enhance environmental, financial and social performance within manufacturing logistics and operations.